Pytorch implementation of Image Style Transfer Using Convolutional Neural Networks. It is my first paper implementation so it would be quite awkward. I recommend you use it only for a reference. The Tutorial Code was very helpful for me to complete my code.
- Image Style Transfer Using Convolutional Neural Networks (2016) [Paper]
- Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis (2016) [Paper]
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution (2016) [Paper]
- Fast Patch-based Style Transfer of Arbitrary Style (2016) [Paper]
- A Learned Representation for Artistic Style (2017) [Paper]
- Real-Time Neural Style Transfer for Videos (2017) [Paper]
- Stereoscopic Neural Style Transfer (2018) [Paper]
- Separating Style and Content for Generalized Style Transfer (2018) [Paper]
To train a model with images you want to merge:
$ python train.py --c_weight=1 \
--s_weight=100000 \
--content_img='images/dancing.jpg' \
--style_img='images/picasso.jpg' \
--size=128 --steps=300
To see all training options, run:
$ python train.py --help
which will print:
usage: train.py [-h] [--content_img CONTENT_IMG] [--style_img STYLE_IMG]
[--size SIZE] [--steps STEPS] [--c_weight C_WEIGHT]
[--s_weight S_WEIGHT]
optional arguments:
-h, --help show this help message and exit
--content_img CONTENT_IMG
--style_img STYLE_IMG
--size SIZE if you want to get more clear pictures, increase the
size
--steps STEPS
--c_weight C_WEIGHT weighting factor for content reconstruction
--s_weight S_WEIGHT weighting factor for style reconstruction
Sooyoung Moon / @symoon94